Abstract
Methods for nonlinear dimensionality reduction have been widely used for different purposes, but they are constrained to single manifold datasets. Considering that in real world applications, like video and image analysis, datasets with multiple manifolds are common, we propose a framework to find a low-dimensional embedding for data lying on multiple manifolds. Our approach is inspired on the manifold learning algorithm Laplacian Eigenmaps - LEM, computing the relationships among samples of different datasets based on an intra manifold comparison to unfold properly the data underlying structure. According to the results, our approach shows meaningful embeddings that outperform the results obtained by the conventional LEM algorithm and a previous close related work that analyzes multiple manifolds.
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Valencia-Aguirre, J., Álvarez-Meza, A., Daza-Santacoloma, G., Acosta-Medina, C., Castellanos-Domínguez, C.G. (2011). Multiple Manifold Learning by Nonlinear Dimensionality Reduction. In: San Martin, C., Kim, SW. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011. Lecture Notes in Computer Science, vol 7042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25085-9_24
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DOI: https://doi.org/10.1007/978-3-642-25085-9_24
Publisher Name: Springer, Berlin, Heidelberg
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